The present invention relates to automated analysis of medical images, and more particularly, to automating various medical image analysis tasks using deep image-to-image network learning.
Medical image analysis involves solving important tasks such as landmark detection, anatomy detection, anatomy segmentation, lesion detection, segmentation and characterization, cross-modality image registration, image denoising, cross-domain image synthesis, etc. Computer-based automation of these medical image analysis tasks brings significant benefits to medical imaging. For example, one such benefit of automating medical image analysis tasks is that it allows structured image reading and reporting for a streamlined workflow, thereby improving image reading outcomes in terms of accuracy, reproducibility, and efficiency. Other benefits of automatic medical image analysis tasks include enabling personalized scanning at a reduced radiation dose, saving examination time and cost, and increasing consistency and reproducibility of the examination.
Currently the technical approaches for various medical image analysis tasks are task-dependent. In other words, for each task among landmark detection, anatomy detection, anatomy segmentation, lesion detection, segmentation and characterization, cross modality image registration, image denoising, cross-domain image synthesis, etc., there are a multitude of technical approaches crafted for such a task. Consequently, the approaches for solving the same task are very diverse in nature. There is no systematic, universal approach to address all of these medical image analysis tasks.